Instructions to use CLMBR/superlative-quantifier-lstm-3 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use CLMBR/superlative-quantifier-lstm-3 with Transformers:
# pip install -U transformers accelerate # Load model directly from transformers import RNNForLanguageModeling model = RNNForLanguageModeling.from_pretrained("CLMBR/superlative-quantifier-lstm-3", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download checkpoint-2747520/training_args.bin from CLMBR/superlative-quantifier-lstm-3: direct link, hf CLI and curl.
- Browser
- Download file 4.28 kB
-
https://huggingface.co/CLMBR/superlative-quantifier-lstm-3/resolve/main/checkpoint-2747520/training_args.bin
- Command line
-
hf download hf://CLMBR/superlative-quantifier-lstm-3/checkpoint-2747520/training_args.bin
-
curl -L -o training_args.bin https://huggingface.co/CLMBR/superlative-quantifier-lstm-3/resolve/main/checkpoint-2747520/training_args.bin
4.28 kB
- Xet hash:
- f23f0d41e22bc2ebfe0139920245c34df71b02c286e4457c6560beb23a2916e8
- Size of remote file:
- 4.28 kB
- SHA256:
- 813c34dbceeb7222e03184ef5479c6f61999addc99b15ca007c85c3a2af15af6
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